Post-script—Retail forecasting: Research and practice

This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail dem...

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Detalles Bibliográficos
Autores principales: Fildes, Robert, Kolassa, Stephan, Ma, Shaohui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Institute of Forecasters. Published by Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534457/
https://www.ncbi.nlm.nih.gov/pubmed/36217499
http://dx.doi.org/10.1016/j.ijforecast.2021.09.012
Descripción
Sumario:This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.

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